Description Usage Arguments Value References Examples
Implements the PASS (Proportion Adaptive Segment Selection) procedure of Jeng et al. (2012). PASS uses a higher criticism statistic to pool the information about the presence or absence of a collective anomaly across the components. It uses Circular Binary Segmentation to detect multiple collective anomalies.
1 2 | pass(x, alpha = 2, lambda = NULL, Lmax = 10, Lmin = 1,
transform = robustscale)
|
x |
An n x m real matrix representing n observations of m variates. |
alpha |
A positive integer > 0. This value is used to stabilise the higher criticism based test statistic used by PASS leading to a better finite sample familywise error rate. Anomalies affecting fewer than alpha components will however in all likelihood escape detection. |
lambda |
A positive real value setting the threshold value for the familywise Type 1 error. The default value is (1.1 {\rm log}(n \times Lmax) +2 {\rm log}({\rm log}(m))) / √{{\rm log}({\rm log}(m))}. |
Lmax |
A positive integer ( |
Lmin |
A positive integer ( |
transform |
A function used to transform the data prior to analysis. The default value is to scale the data using the median and the median absolute deviation. |
An S4 object of type .pass.class
containing the data X
, procedure parameter values, and the results.
10.1093/biomet/ass059anomaly
1 2 3 4 5 |
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